Alireza Ghazavi Khorasgani
Academic and research departments
Institute for Communication Systems, School of Computer Science and Electronic Engineering, Faculty of Engineering and Physical Sciences.About
My research project
Integrated Sensing and Communication (ISAC)Many applications envisioned for future communication networks (5G+/6G and beyond) aim to incorporate autonomous control and management systems into communications. This requires the ability to sense the dynamic environment and react appropriately, for intelligent communications.
Integrated communication and sensing (ISAC) is a promising approach to integrate the sensing and communication functionalities by sharing all or part of the bandwidth, transmitted signal waveform, hardware, network infrastructure, and processing framework.
The goal of this project is to investigate the fundamental limits of ISAC systems by characterizing the theoretical performance metrics (such as the ergodic capacity and outage probability) in large random networks, where a large number of users are randomly located. The first step is to develop a mathematical framework and then to characterize the interaction between different key performance indices (KPIs). This enables us to find the optimal regions (boundaries) for the performance metrics, resolve the trade-offs.
Supervisors
Many applications envisioned for future communication networks (5G+/6G and beyond) aim to incorporate autonomous control and management systems into communications. This requires the ability to sense the dynamic environment and react appropriately, for intelligent communications.
Integrated communication and sensing (ISAC) is a promising approach to integrate the sensing and communication functionalities by sharing all or part of the bandwidth, transmitted signal waveform, hardware, network infrastructure, and processing framework.
The goal of this project is to investigate the fundamental limits of ISAC systems by characterizing the theoretical performance metrics (such as the ergodic capacity and outage probability) in large random networks, where a large number of users are randomly located. The first step is to develop a mathematical framework and then to characterize the interaction between different key performance indices (KPIs). This enables us to find the optimal regions (boundaries) for the performance metrics, resolve the trade-offs.
My qualifications
Affiliations and memberships
IEEE Young Professionals
ResearchResearch interests
- Integrated Sensing and Communication (ISAC)
- Information Theory
- Digital Communication
- Wireless Communication
- Convex Optimization
- Estimation Theory
- Intelligent Reflecting Surfaces (IRS)
Research interests
- Integrated Sensing and Communication (ISAC)
- Information Theory
- Digital Communication
- Wireless Communication
- Convex Optimization
- Estimation Theory
- Intelligent Reflecting Surfaces (IRS)
Teaching
University of Surrey
Laboratory Demonstrator
Fundamentals of Digital Signal Processing (EEE3008)
Autumn Semester, 2024
Under Professor Mark Plumbley
Demonstrating support
FEPS Foundation Year, including the modules ENG0018 and ENG0019.
Autumn Semester, 2024
Under Dr Richard Harrison
Laboratory Demonstrator
Advanced Satellite Communication Techniques (EEEM032)
2023-24
Under Professor Zhili Sun
Isfahan University of Technology (ECE Department)
Lab Demonstrator and Teaching Assistant
- Responsible for designing and correcting homework assignments for several Bachelor’s and Master’s courses.
Publications
Intelligent Reflecting Surface (IRS) technology is revolutionizing wireless communications by shifting from channel adaptation to a responsive wireless environment. This paper introduces a multi-IRS assisted millimeter wave (mm-wave) system, allowing intelligent on/off control of individual IRS elements. Our objective is to optimize energy efficiency under Quality of Service (QoS) constraints. We propose an algorithm where the Access Point (AP) adjusts transmit beamforming, and IRS elements control phaseshifts and on/off status until convergence. Utilizing a fractional programming (FP) approach for AP beamforming and Simulated Annealing (SA) for IRS subproblems, we achieve a suboptimum optimal solution. A modified nested FP approach addresses the beamforming subproblem. Performance analysis in a practical scenario reveals a significant up to 132.16\% improvement in energy efficiency compared to scenarios with randomly selected IRS on/off status. This highlights the efficacy of our algorithm in enhancing mm-wave communication systems' overall efficiency.
This paper characterizes the optimal capacity-distortion (C-D) tradeoff in an optical point-to-point (P2P) system with single-input single-output for communication and single-input multiple-output for sensing (SISO-COM and SIMO-SEN) within an integrated sensing and communication (ISAC) framework. We consider the optimal rate-distortion (R-D) region and explore several inner (IB) and outer (OB) bounds. We introduce practical, asymptotically optimal maximum a posteriori (MAP) and maximum likelihood estimators (MLE) for target distance, addressing nonlinear measurement-to-state relationships and non-conjugate priors. As the number of sensing antennas increases, these estimators converge to the Bayesian Cram'er-Rao bound (BCRB). We also establish that the achievable rate-CRB (AR-CRB) serves as an OB for the optimal C-D region, valid for both unbiased estimators and asymptotically large numbers of receive antennas. To clarify that the input distribution determines the tradeoff across the Pareto boundary of the C-D region, we propose two algorithms: i) an iterative Blahut-Arimoto algorithm (BAA)-type method, and ii) a memory-efficient closed-form (CF) approach. The CF approach includes a CF optimal distribution for high optical signal-to-noise ratio (O-SNR) conditions. Additionally, we adapt and refine the Deterministic-Random Tradeoff (DRT) to this optical ISAC context.
This study introduces a novel approach for energy-efficient resource allocation in millimeter-wave networks, assisted by multiple intelligent reflecting surfaces (IRS). The proposed framework optimizes the dynamic ON/OFF control and phase shifts of Intelligent Reflecting Surfaces (IRS) elements, along with beamforming (BF) at Access Points (AP), under practical constraints. Unlike existing approaches, our model enhances Energy Efficiency (EE) by optimizing a fixed number of ON IRS elements. We propose innovative algorithms, including modified nested fractional programming (NFP) for BF and Simulated Annealing (SA)-type algorithm for phase shift and element selection. Our framework satisfies quality-of-service (QoS) requirements while addressing practical IRS deployment limitations. Results show a 132.17% improvement in EE under realistic scenarios, highlighting the potential of our method as a key strategy for future 6G networks.
In this paper, an unmanned aerial vehicle (UAVs)-assisted visible light communication (VLC) has been considered which has two tiers: UAV-to-centroid and device-to-device (D2D). In the UAV-to-centroid tier, each UAV can simultaneously provide communications and illumination for the centroids of the ground users over VLC links. In the D2D tier, the centroids retransmit received data from UAV over D2D links to the cluster members. For network, the optimization problem of joint user association and deployment location of UAVs is formulated so as to maximize the received data, satisfy illumination constraints, and also the user cluster size. An iterative algorithm is first proposed to transform the optimization problem into a series of two interdependent sub problems. Following the smallest enclosing disk theorem, a random incremental construction method is designed to find the optimal UAV locations. Then, inspired by unsupervised learning method, a clustering algorithm to find a suboptimal user association is proposed. Our simulation results show that the proposed scheme on average guarantees the users brightness 0.3 microwatt more than their threshold requirements. Moreover, the received bitrate plus number of D2D connected users under our proposed method is 55.0% more than the scenario in which we do not optimize UAV location.